pgvector vs Milvus for AI agents: Which Should You Use?

pgvector is the “keep it in Postgres” option: simple, familiar, and good enough when your agent stack is already centered on a relational database. Milvus is a dedicated vector database built for large-scale similarity search, filtering, and retrieval throughput.

For AI agents, start with pgvector unless you already know you need Milvus-level scale or retrieval performance.

Quick Comparison

When pgvector Wins

• •

  Your agent already lives in Postgres

  If your app stores users, sessions, tool outputs, audit logs, and business data in PostgreSQL, pgvector is the obvious move. You can add embeddings with ALTER TABLE ... ADD COLUMN embedding vector(1536); and keep everything in one place.

• •

  You need SQL joins with retrieval

  AI agents rarely just do vector search. They usually need metadata filters, tenant isolation, permissions checks, and joins to business tables. pgvector handles this cleanly with normal SQL:

  SELECT id, content
  FROM documents
  WHERE tenant_id = $1
    AND embedding <-> $2 < 0.3
  ORDER BY embedding <-> $2
  LIMIT 5;
  

• •

  You want fewer moving parts

  For production agent systems, every new datastore becomes another failure domain. pgvector keeps deployment simple: one backup strategy, one auth model, one monitoring stack.

• •

  Your workload is moderate

  If you are building a support agent over internal knowledge bases, a claims assistant over policy docs, or a CRM copilot with tens of thousands to a few million chunks, pgvector is enough. Use hnsw or ivfflat, tune your queries properly, and stop overengineering.

When Milvus Wins

• •

  You have serious retrieval scale

  If your agent system needs millions to billions of vectors and high concurrency, Milvus is the right tool. It is designed for ANN search at scale instead of being bolted onto an OLTP database.

• •

  Vector search is the product

  If retrieval quality and throughput are core to the business — not just an internal feature — use Milvus. It gives you a dedicated architecture for vector-heavy workloads instead of competing with transactional queries.

• •

  You need distributed infrastructure

  Milvus makes sense when one database node is not enough and you want scaling patterns built around vector workloads. That matters for large enterprise agents serving many tenants or many teams at once.

• •

  You expect aggressive filtering plus large datasets

  Milvus handles production retrieval scenarios where you combine metadata filters with nearest-neighbor search across huge corpora. If your AI agents depend on fast top-k lookups over massive indexes all day long, Postgres will eventually become the wrong place to force that work.

For AI agents Specifically

Use pgvector first unless you have hard evidence that retrieval scale will hurt you. Most AI agents need tight integration with application data more than they need specialized vector infrastructure.

Milvus is the better choice when your agent platform is already operating like a search engine: huge corpus, high QPS, strict latency targets, and dedicated infrastructure budget. Otherwise, pgvector gets you to production faster with less operational drag and fewer systems to babysit.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.